Evolving artificial intelligence
Evolving artificial intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Intelligence through simulated evolution: forty years of evolutionary programming
Intelligence through simulated evolution: forty years of evolutionary programming
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
A single-point mutation evolutionary programming
Information Processing Letters
DE/EDA: a new evolutionary algorithm for global optimization
Information Sciences—Informatics and Computer Science: An International Journal
Human evolutionary model: A new approach to optimization
Information Sciences: an International Journal
An overview of evolutionary algorithms for parameter optimization
Evolutionary Computation
A study of particle swarm optimization particle trajectories
Information Sciences: an International Journal
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Evolutionary programming using mutations based on the Levy probability distribution
IEEE Transactions on Evolutionary Computation
Oppositional biogeography-based optimization
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Empirical analysis of an on-line adaptive system using a mixture of Bayesian networks
Information Sciences: an International Journal
Hessian matrix distribution for Bayesian policy gradient reinforcement learning
Information Sciences: an International Journal
Function optimisation by learning automata
Information Sciences: an International Journal
Information Sciences: an International Journal
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This paper studies evolutionary programming and adopts reinforcement learning theory to learn individual mutation operators. A novel algorithm named RLEP (Evolutionary Programming based on Reinforcement Learning) is proposed. In this algorithm, each individual learns its optimal mutation operator based on the immediate and delayed performance of mutation operators. Mutation operator selection is mapped into a reinforcement learning problem. Reinforcement learning methods are used to learn optimal policies by maximizing the accumulated rewards. According to the calculated Q function value of each candidate mutation operator, an optimal mutation operator can be selected to maximize the learned Q function value. Four different mutation operators have been employed as the basic candidate operators in RLEP and one is selected for each individual in different generations. Our simulation shows the performance of RLEP is the same as or better than the best of the four basic mutation operators.